Wireless connectivity among vehicles, infrastructure, and mobile devices has brought about innovative solutions to improve the safety and mobility of transportation systems. One of the more promising solutions utilizes the deployment of Bluetooth sensors that anonymously use the machine access control address of a cell phone without privacy concerns. However, on arterial networks, latency issues hinder the full potential of Bluetooth technology, and because individuals must enable discovery mode on their mobile phones for the technology to work, market penetration using Bluetooth technology is relatively flat. An alternative solution utilizes connected vehicle (CV) technology, which allows a vehicle to share data with devices inside the vehicle and to other devices outside of the vehicle, such as another vehicle or roadside sensor.
Some CV technology includes dedicated short-range communications (DSRC) that enable onboard equipment (OBE) of a vehicle to interact with the OBE of other vehicles and to transmit information to the roadside sensors. However, despite the significant potential benefits of optimizing sensor locations, the challenges associated with identifying optimal sensor locations for multiple time stages throughout a day with uncertain demand patterns have received little attention.
Conventional applications related to sensor positioning have focused on locating permanently installed sensors to enhance the quality of traffic origin-destination (OD) demand or travel time estimations. These permanently installed sensors may produce meaningful information for traffic management, but are constrained by their lack of portability. For example, a permanently installed sensor that may provide useful data during the morning rush hour, but may produce meaningless information in the afternoon when traffic patterns change. In practice, sensors are often located at locations with high likelihood of recurrent congestion during peak or off-peak periods. However, given cost considerations associated with purchasing and installing the sensors, it is not economically feasible to permanently install the sensors at every congested location.
Typically, to identify where to locate and install a roadside sensor, i.e., the sensor location problem (SLP), involves selecting certain arcs or nodes for the sensors. Depending on the traditional detection technologies (e.g., loop, image, fixed vehicle identification (ID) and more recent technologies (e.g., portable vehicle and path ID), the existing problems differ according to different types of sensors and measurement of interests. Based on the capability of a sensor, traffic measurements that have been used in SLP studies are (1) OD flow observability, (2) OD flow estimation, (3) travel time, and (4) signal control.
OD flow observability, inspired by covering location models, provides full or partial flow observability for the sensor coefficient matrix. Two types of OD flow observability problems may include: full flow-observability problems having counting sensors located on links to observe either OD trips or route/link flows, or located on nodes and known split ratios; and partial flow-observability problems having path ID sensors located on links to observe route flows or vehicle ID sensors located on the links of the network. OD flow estimation estimates the traffic flow without full rank to overcome the underestimation. The third problem attempts to find different sensor location layouts to minimize the traffic measurement errors such as density and flow. The fourth problem utilizes adaptive traffic control to estimate incoming volumes and queue blocking probability using traditional sensors.
These SLP problems concentrate on traffic volume coverage with maximum information gain at permanent sensor locations. However, traditional traffic volume detectors have several disadvantages, which require extensive modeling efforts to quantify the uncertainty generated by the detectors. For example, as up to half of inductance loop detectors may malfunction during a given time period, advanced algorithms are employed to overcome measurement errors of the inductance loop detectors (single and dual). However, making adjustments to inductance loop detectors and video detection in order to provide the level of detection needed to be fully adaptive to real-time traffic is oftentimes inaccurate, expensive, and unreliable. Additionally, the adjustments of the detectors can be limited in physical range.
As an alternative, traffic signal coordination may utilize Bluetooth sensors placed along roads that can track Bluetooth devices in passing vehicles, which may detect and record how long a car takes to drive along a corridor, segment by segment. Compared to the traditional method, depending on the point speed at sensors fixed locations, Bluetooth technology may provide point-to-point travel time over the segments. However, for arterial signal control, this point-to-point detection-based Bluetooth technology still has latency issues.
There is a specific need to optimize the location and deployment of roadside sensors under budget. Specifically, it is essential to decide where best to locate sensors to maximize the benefit of CV deployment.